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RFCNet:Remote Sensing Image Super-Resolution Using Residual Feature Calibration Network

查看全文 作　　者：Yuan [1]Xue;Liangliang [2]Li;Zheyuan [1]Wang;Chenchen [1]Jiang;Minqin [1]Liu;Jiawen [3]Wang;Kaipeng [3]Sun;Hongbing [2]Ma 高影响力作者 机构地区：[1]College of Information Science and Engineering,Xinjiang University,Urumqi 830046,China;[2]Department of Electronic Engineering,Tsinghua University,Beijing 100084,China;[3]Shanghai Institute of Satellite Engineering,Shanghai 201109,China高影响力机构 出　　处：《Tsinghua Science and Technology》索引2023年第28卷第3期,共11页高影响力期刊 基　　金：This work was supported by Shanghai Aerospace Science and Technology Innovation Fund(No.SAST2019-048);the Cross-Media Intelligent Technology Project of Beijing National Research Center for Information Science and Technology(BNRist)(No.BNR2019TD01022). 摘　　要：In the field of single remote sensing image Super-Resolution(SR),deep Convolutional Neural Networks(CNNs)have achieved top performance.To further enhance convolutional module performance in processing remote sensing images,we construct an efficient residual feature calibration block to generate expressive features.After harvesting residual features,we first divide them into two parts along the channel dimension.One part flows to the Self-Calibrated Convolution(SCC)to be further refined,and the other part is rescaled by the proposed Two-Path Channel Attention(TPCA)mechanism.SCC corrects local features according to their expressions under the deep receptive field,so that the features can be refined without increasing the number of calculations.The proposed TPCA uses the means and variances of feature maps to obtain accurate channel attention vectors.Moreover,a region-level nonlocal operation is introduced to capture long-distance spatial contextual information by exploring pixel dependencies at the region level.Extensive experiments demonstrate that the proposed residual feature calibration network is superior to other SR methods in terms of quantitative metrics and visual quality. 关 键 词：Convolutional Neural Network(CNN) remote sensing image Super-Resolution(SR) attention mechanism
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